Abstract

AbstractGlobal drylands are experiencing booming development of centralized photovoltaics (PV), which aims to address the dual challenges posed by climate change and energy transformation. In dryland areas with large‐scale deployment of solar PV infrastructure, vegetation was reported to experience drastic changes. However, the long‐term dynamic changes and driving mechanisms have not been thoroughly studied yet. Quantitatively distinguishing the disturbances of climate change and PV plant deployment on vegetation change is the key to understanding the environmental impact of clean energy development and formulating adaptive ecological recovery measures. To understand this, we selected the Gonghe solar power project in northern China, one of the largest dryland PV plants in the world, as a case study. Specifically, satellite‐derived Normalized Difference Vegetation Index (NDVI) and meteorological data from ground stations were used to analyze the changing patterns of vegetation growth and climatic factors. The relative contributions of climatic factors and PV plant deployment to NDVI change were quantified by multiple regression analysis. The results indicated that vegetation has increased gradually since 2000, with the rate of vegetation recovery doubled during the PV expansion period. From 2013 to 2020, climate change was the main driver of increased vegetation (56%), followed by the expansion of solar PV infrastructures (44%). Vegetation inside PV arrays increased 1.4 times faster than outside, mainly because the PV panels improve the efficiency of rainwater utilization in summer and reduce the negative impact of excessive sunlight in the growing season. In addition, vegetation management practices like grazing can further enhance carbon sequestration and create sustainable livelihood opportunities, achieving sustainable economic, social, and ecological development. The novelty of the study lies in the proposed framework to quantify the impact of solar PV programs on vegetation in dryland allowing easy interpretations of vegetation dynamics under clean energy development and climate change, which provide scientific references for clean energy planning and ecological recovery in arid areas.

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